In agent systems, meta-level reasoning is commonly used in enforcing rationality in the choice of goals and actions performed by an agent, ensuring that an agent behaves as effectively and efficiently as possible. Through metareasoning an agent is able to explicitly consider goals before committing to them, and consider courses of action before executing plans. In this paper, we argue that although seldom considered, a flexible meta-level reasoning component is a valuable addition to any agent architecture. We describe such a component for use in BDI architectures, underpinned by a model of motivation and a motivationbased description language, and demonstrate its effectiveness empirically. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Meneguzzi, F., & Luck, M. (2007). Motivations as an abstraction of meta-level reasoning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4696 LNAI, pp. 204–214). Springer Verlag. https://doi.org/10.1007/978-3-540-75254-7_21
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